An End-to-End Network for Co-Saliency Detection in One Single Image

• URL: http://arxiv.org/abs/1910.11819v2
• Date: Wed, 15 Feb 2023 15:17:28 GMT
• Title: An End-to-End Network for Co-Saliency Detection in One Single Image
• Authors: Yuanhao Yue, Qin Zou, Hongkai Yu, Qian Wang, Zhongyuan Wang and Song Wang
• Abstract summary: Co-saliency detection within a single image is a common vision problem that has not yet been well addressed. This study proposes a novel end-to-end trainable network comprising a backbone net and two branch nets. We construct a new dataset of 2,019 natural images with co-saliency in each image to evaluate the proposed method.
• Score: 47.35448093528382
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Co-saliency detection within a single image is a common vision problem that has received little attention and has not yet been well addressed. Existing methods often used a bottom-up strategy to infer co-saliency in an image in which salient regions are firstly detected using visual primitives such as color and shape and then grouped and merged into a co-saliency map. However, co-saliency is intrinsically perceived complexly with bottom-up and top-down strategies combined in human vision. To address this problem, this study proposes a novel end-to-end trainable network comprising a backbone net and two branch nets. The backbone net uses ground-truth masks as top-down guidance for saliency prediction, whereas the two branch nets construct triplet proposals for regional feature mapping and clustering, which drives the network to be bottom-up sensitive to co-salient regions. We construct a new dataset of 2,019 natural images with co-saliency in each image to evaluate the proposed method. Experimental results show that the proposed method achieves state-of-the-art accuracy with a running speed of 28 fps.

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